Artificial Intelligence Consultant

Adopt and create the integration of AI, ML, and DevOps practices within complex business domains. You will collaborate with cross-functional teams, including data engineers, developers, operations, IT, and business leadership, to align AI strategies with enterprise goals and aspirations.

Responsibilities

• Collaborate with executive leadership to develop and refine the AI strategy, ensuring alignment with business goals and industry best practices.
• Provide strategic leadership in designing and implementing scalable and secure AI architectures for the Enterprise.
• Lead the design and implementation of AI solutions, leveraging cutting-edge technologies and frameworks to address complex business challenges. Influence and establish best practices through solid design decisions, processes, and tools. Set goals and strategies and oversee the deployment of large-scale projects across multiple technologies.
• Collaborate with cross-functional teams, including engineers and business stakeholders to ensure seamless integration of AI solutions with existing systems and processes.
• Establish governance frameworks for AI initiatives, ensuring compliance with regulatory standards and industry guidelines.
• Stay abreast of emerging AI technologies, assess their potential impact on the enterprise, and make recommendations for adoption by doing rapid prototyping.
• Provide guidance and mentorship to the AI team, fostering a culture of innovation, collaboration, and continuous learning. Leading contributors individually and as team members, providing direction and mentoring to others.
• Lead various Security and Architecture reviews for complex solutions and integrations for the team. Work closely with security and risk leaders to foresee and overturn risks, such as training data poisoning, AI model theft, and adversarial samples, ensuring ethical AI implementation and restoring trust in AI systems. Remain acquainted with upcoming regulations and map them to best practices.
• Provide rapid prototyping for various concept projects before they can be prioritized, including developing, training, fine-tuning, and deploying large multimodal language models for retrieval augmented generation.
• Apply instruction tuning, reinforcement learning from human feedback (RLHF), and parameter-efficient fine-tuning such as p-tuning, adaptors, LoRA, and so on to improve LLMs for different domain-specific RAG use cases.

Qualifications

• Bachelors, Master's, or Ph.D. in Computer Science, Artificial Intelligence, or a related field.
• 10+ years’ proven experience as an Enterprise Architect, Solution Architect, or a similar role, focusing on AI/ML and generative AI in the last 5+ years.
• 5+ years of hands-on experience in building and deploying Machine Learning solutions using various supervised/unsupervised ML algorithms such as Linear/Logistic Regression, Support Vector Machines, (Deep) Neural Networks, Random Forest, etc., and hands-on experience with Python programming and statistical packages, and ML libraries such as scikit-learn, Keras, TensorFlow, PyTorch, MXNet, etc. and/or natural language processing using NLTK, spaCy, Gensim, etc.
• 4+ years experience building IT use cases/solutions, especially around AI/ML cognitive services/ platforms, Model productionization, ML Ops, CICD Automation, Cloud/On-prem environments,
• Proficient in programming languages such as Python, R, Java, etc.
• Experience working with RAG technologies such as LLM frameworks (Langchain and LLamaIndex), LLM model registries (Hugging Face), LLM APIs, embedding models, and vector databases (FAISS and Milvus).
• Understanding key libraries used for LLM and RAG development: for NLP models development (e.g., NeMo, DeepSpeed, HuggingFace), for deployment (e.g., TensorRT-LLM, Triton Inference Server) for Information Retrieval (e.g., RAPIDS, Milvus, Pinecone, Open Search).
• Experience working with larger transformer-based architectures for NLP, CV, ASR, or others.
• Experience interacting with REST APIs and microservices.
• Strong understanding of machine learning, deep learning, computer vision, natural language processing concepts, and speech recognition concepts.
• Deep expertise in cloud-based AI services like OCI Data Science Platform, OCI AI Services, Oracle Digital Assistant, AWS AI Services or Azure AI Services
• Solid understanding of machine learning principles, including advanced analytics tools, applied mathematics, ML and Deep Learning frameworks and libraries, and ML techniques.
• Proficient in using Jupyter Notebooks for all sorts of data science tasks such as exploratory data analysis (EDA), data cleaning and transformation, data visualization, statistical modeling, machine learning, and deep learning.
• Hands-on experience with cloud-based platforms and services, such as OCI, AWS, GCP, and Azure.
• Proven ability to optimize LLM models for inference speed, memory efficiency, and resource utilization.
• Experience deploying LLM models in cloud environments (e.g., OCI, AWS, Azure, GCP) and on-premises infrastructure.
• Familiarity with containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) for scalable and efficient model deployment.
• Strong knowledge of GPU cluster architecture and the ability to leverage parallel processing for accelerated model training and inference.